Can you deploy AI/ML to make sense from high volume Documentation?

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Can you deploy AI/ML to make sense from high volume Documentation?

The HBR article by Wilson, Daugherty, and Davenport on “The Future of AI Will Be About Less Data, Not More” essentially argue on the requirements of the smarter companies looking for more “human” systems. Companies looking to invest in Artificial Intelligence (AI) capabilities should foresee the need to make machines more intelligent and less artificial. The current AI, in most cases, fails to handle the edge cases; e.g., the mobile facial recognition systems failing to recognize the morning face, or a driverless car failing to recognize unusually dressed humans, etc. The approach of machines to solve a problem or perform a task should ideally resemble the way a human would do: the top-down reasoning approach using limited data instead of the bottom-up big data approach. Such reasoning abilities in AI will also create opportunities for early adopters in businesses, as they will have better expertise and common sense.

A hot application of AI and Machine Learning (ML) in a hyper-automated extraction-analysis-categorization process of high-volume structured/ unstructured documents is a technology called Intelligent Document Processing (IDP). Key business processes like contract management, invoicing, and customer service need efficient and streamlined document processing using natural language processing (NLP) and computer vision technology (CVT). A typical IDP workflow would begin with taking necessary inputs from the scanned documents or images and extracting and cleansing the useful content using optical character recognition (OCR). This content is processed using AI/ML tools like NLP, CVT, deep learning, etc. Usually, at the end of such model execution is a human interface that verifies the output and approves it for integration with the business workflow if the confidence score is sufficiently high.

An IDP would recognize the content and structure of the given documents to retrieve key information and also categorize the documents automatically. Such automated information retrieval is very helpful in setting automatic triggers for further business actions like payments, approvals, etc. Cloud-based IDP solutions are furthermore cost-effective, scalable, and flexible. Gartner estimated the IDP market to be sizing almost $4.8 billion in 2022, rising from $1.2 billion in 2020.

Business processes in various industries like Healthcare, Banking/ Financial services, Government, Manufacturing, Human resources, etc., greatly benefit in terms of reduced human intervention, improved accuracy, data retrieval, and analysis. Organizations need to explore their use cases of IDP to integrate the results into their business workflow, thereby improving their profitability.

About Teqfocus: Teqfocus is known to bridge the gaps between business solutions and technology execution; and can provide efficient IDP solutions to make your business processes much more efficient and profitable.

Disclaimer: This document is professionally hand-edited and has been driven by research conducted by experts from both industry and academia.